The ranking of negative-cost emissions reduction measures

A flaw has been identified in the calculation of the cost-effectiveness in marginal abatement cost curves (MACCs). The problem affects “negative-cost” emissions reduction measures—those that produce a return on investment. The resulting ranking sometimes favours measures that produce low emissions savings and is therefore unreliable. The issue is important because incorrect ranking means a potential failure to achieve the best-value outcome. A simple mathematical analysis shows that not only is the standard cost-effectiveness calculation inadequate for ranking negative-cost measures, but there is no possible replacement that satisfies reasonable requirements. Furthermore, the concept of negative cost-effectiveness is found to be unsound and its use should be avoided. Among other things, this means that MACCs are unsuitable for ranking negative-cost measures. As a result, MACCs produced by a range of organizations including UK government departments may need to be revised. An alternative partial ranking method has been devised by making use of Pareto optimization. The outcome can be presented as a stacked bar chart that indicates both the preferred ordering and the total emissions saving available for each measure without specifying a cost-effectiveness

Emissions reductions in hotels in 2030

Detailed simulations of two hotels have been carried out, to determine whether CO2 emissions can be reduced by 50%. The hotels, one older and converted and the other newer and purpose-built, were chosen to represent the most common UK hotel types. The effects were studied of interventions expected to be available in 2030 including fabric improvements, HVAC changes, lighting and appliance improvements and renewable energy generation. The main finding was that it is technically feasible to reduce emissions by 50% without compromising guest comfort. Ranking of the interventions was problematical for several reasons including interdependence and the impacts on boiler sizing of large reductions in the heating load

Evaluation of refurbishment strategies for post-war office buildings

Multiple combinations of energy saving refurbishment measures were applied to representative models of post-war office buildings using EnergyPlus. Based on energy consumption, thermal comfort and costs, a range of heating and cooling refurbishment features were evaluated under a parameter study. The evaluation shows that although refurbished post-war offices with high insulation consume negligible amounts of heating energy, thermal comfort could only be provided by additional active cooling which results in higher costs and lower greenhouse gas reductions

Enabling urban-scale energy modelling: a new spatial approach

Urban-scale energy modelling provides an ideal tool for studying non-domestic energy consumption and emissions reduction at the community level. In principle, an approach based on the characteristics of individual commercial premises and buildings is attractive, but it poses a number of challenges, the most immediate of which is deciding precisely what to model. For a range of reasons connected with their self-contained nature, individual non-domestic buildings would ideally be selected. However, the main information sources available - digital mapping and business taxation data - are not based on 'buildings' and do not use the concept, thus making an automated approach problematic. At the same time, manual identification of the distinct buildings in a city is not a practical proposition because of the numbers involved. The digital mapping and business taxation data are brought together in the Local Land and Property Gazetteer (LLPG). An analysis of the relationships between the relevant elements, namely building polygons and premises attracting business taxation, allowed a unit to be defined that matches the definition of a 'building' in most circumstances and can be applied without the need for human intervention. This novel approach provides a firmer basis for modelling non-domestic building energy at the urban scale

Multi-dwelling refurbishment optimization: problem decomposition, solution and trade-off analysis

A methodology has been developed for the multiobjective optimization of the refurbishment of domestic building stock on a regional scale. The approach is based on the decomposition of the problem into two stages: first to find the energy-cost trade-off for individual houses, and then to apply it tomultiple houses. The approach has been applied to 759 dwellings using buildings data from a survey of the UK housing stock. The energy use of each building and their refurbished variants were simulated using EnergyPlus using automatically-generated input files. The variation in the contributing refurbishment options from least to highest cost along the Pareto front shows loft and cavity wall insulation to be optimal intially, and solid wall insulation and double glazing appearing later

Simulation of energy use in UK supermarkets using EnergyPlus

This paper investigates the interaction between supermarket heating, ventilation and air conditioning and refrigeration systems through simulation in EnergyPlus. This interaction has been studied by modelling a generic UK supermarket. The impact on the sum of HVAC and refrigeration energy consumption due to changes in a range of operating conditions was studied. These include the effect of altering HVAC temperature set-points, supply air temperatures and refrigeration case operating temperatures on their overall energy use. Optimum values of the supply air temperature, to minimise CO2 emissions, delivered by the HVAC system were found to vary with UK location, with typical values around 14oC to 16oC

Developing a geographically detailed housing stock model for the North East of England

Housing stock models predict long term changes in the stock to inform national policy. They operate with a set of reference dwellings representing the national stock, which are changed in response to different scenarios. However, national level models do not consider geographical variations (urban location/rural surroundings, index of multiple deprivation score, etc.), so cannot aid in targeting improvement measures (eg: insulation, microgeneration, etc.) locally. A geographically varying model can identify which measures are most appropriate in a particular location. In this paper a method has been designed and implemented using information at LSOA level (c. 700 dwellings each) to introduce geographical variation for a model of the North East of England. It has been tested against DECC meter data and over 80% of LSOAs are predicted to within ±25% of DECC’s data. The model allows localised policies and interventions to be tested, and is principally of interest to local government and energy efficiency initiatives

Coupling a stochastic occupancy model to EnergyPlus to predict hourly thermal demand of a neighbourhood

When designing and managing integrated renewable energy technologies at a community level, prediction of hourly thermal demand is essential. Dynamic thermal modelling, using deterministic occupancy profiles, has been widely used to predict the highresolution temporal thermal demand of individual buildings. Only in recent years has this approach started to be applied to simulate all buildings in a neighbourhood or an entire housing stock of a region. This study explores the potential of predicting hourly thermal demand for a group of dwellings by applying a stochastic occupancy model to dynamic thermal modelling. A case study with 125 new houses demonstrates the approach. The result was a more realistic and representative hourly thermal demand profile, compared to using standard deterministic occupancy profiles

Dynamic modelling of a large scale retrofit programme for the housing stock in the North East of England

Housing stock models have long been employed to estimate the baseline energy demand of the existing housing stock, as well as to predict the effectiveness of applying different retrofit measures and renewable technologies on reducing the energy demand and corresponding CO2 emissions. This research aims to develop a dynamic housing stock model to simulate the hourby-hour energy demands of 1.2 million dwellings in the North East (NE) of England using the 2008-9 English Housing Survey (EHS) data. The model is validated by comparison to a steady-state energy model. Using the model, new results predicting the impact of a large scale retrofit programme for the NE housing stock are generated

Dynamic energy modelling of UK housing: evaluation of alternative approaches

Models of UK houses have been created at nine levels of details, to study the impacts on the computed energy consumption. The results will inform an evaluation of alternative methods of large-scale dynamic stock modelling. The levels reflect the shortage of suitable data at one end of the scale and the effort involved in implementing a detailed model at the other..